mapreduce分区

本次分区是采用项目垃圾分类的csv文件，按照小于4的分为一个文件，大于等于4的分为一个文件

源代码：

PartitionMapper.java:

package cn.idcast.partition;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;
/*
K1:行偏移量 LongWritable
v1:行文本数据    Text

k2:行文本数据 Text
v2:NullWritable
 */

public class PartitionMapper extends Mapper<LongWritable,Text, Text, NullWritable> {
    //map方法将v1和k1转为k2和v2
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
       context.write(value,NullWritable.get());
    }
}

PartitionerReducer.java:

package cn.idcast.partition;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;

/*
k2: Text
v2: NullWritable

k3: Text
v3: NullWritable
 */
public class PartitionerReducer extends Reducer<Text, NullWritable,Text, NullWritable> {
    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        context.write(key,NullWritable.get());
    }
}

MyPartitioner.java:

package cn.idcast.partition;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

public class MyPartitioner extends Partitioner<Text, NullWritable> {
    /*
    1:定义分区规则
    2:返回对应的分区编号

     */
    @Override
    public int getPartition(Text text, NullWritable nullWritable, int numPartitions) {
        //1:拆分行文本数据(k2),获取垃圾分类数据的值
        String[] split = text.toString().split(",");
        String numStr=split[1];

        //2:判断字段与15的关系，然后返回对应的分区编号
        if(Integer.parseInt(numStr)>=4){
            return 1;
        }
        else{
            return 0;
        }
    }
}

JobMain.java:

package cn.idcast.partition;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.net.URI;

public class JobMain extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        //1:创建Job任务对象
        Job job = Job.getInstance(super.getConf(), "partition_mapreduce");
        //如果打包运行出错，则需要加该配置
        job.setJarByClass(cn.idcast.mapreduce.JobMain.class);
        //2:对Job任务进行配置(八个步骤)
            //第一步:设置输入类和输入的路径
            job.setInputFormatClass(TextInputFormat.class);
            TextInputFormat.addInputPath(job,new Path("hdfs://node1:8020/input"));
            //第二部:设置Mapper类和数据类型(k2和v2)
            job.setMapperClass(PartitionMapper.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(NullWritable.class);
            //第三步:指定分区类
            job.setPartitionerClass(MyPartitioner.class);
            //第四、五、六步
            //第七步:指定Reducer类和数据类型(k3和v3)
            job.setReducerClass(PartitionerReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(NullWritable.class);
            //设置ReduceTask的个数
            job.setNumReduceTasks(2);
            //第八步:指定输出类和输出路径
            job.setOutputFormatClass(TextOutputFormat.class);
            Path path=new Path("hdfs://node1:8020/out/partition_out");
            TextOutputFormat.setOutputPath(job,path);
            //获取FileSystem
            FileSystem fs = FileSystem.get(new URI("hdfs://node1:8020/partition_out"),new Configuration());
            //判断目录是否存在
            if (fs.exists(path)) {
                fs.delete(path, true);
                System.out.println("存在此输出路径，已删除！！！");
            }
        //3:等待任务结束
        boolean bl = job.waitForCompletion(true);
        return bl?0:1;
    }

    public static void main(String[] args) throws Exception {
        Configuration configuration = new Configuration();
        //启动一个job任务
        int run = ToolRunner.run(configuration, new JobMain(), args);
        System.exit(run);
    }
}

在hadoop或者本地运行结果：

1.均为4-16的文件

2.均为1-3的文件